Initial assessment of IPW data from SOI-CORS stations over Uttar Pradesh

Abstract

This study explores the impact of assimilating Integrated Precipitable Water (IPW) data from the Survey of India (SOI) – Continuously Operating Reference Stations (CORS) into weather forecasts using the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) as the assimilation scheme. IPW data from SOI-CORS stations located in Uttar Pradesh and neighbouring regions. The research was conducted over June and July 2024, with a high-resolution WRF model domain (3 km grid spacing) focussing on the rainfall events during the period. Data assimilation was performed four times daily at 0000, 0600, 1200, and 1800 UTC, with forecasts extending up to 72 hours. The study found that assimilating IPW data significantly improved both the analysis and the forecasts of various meteorological parameters. Comparison with radiosonde showed a 2% reduction in Root Mean Square Error (RMSE) of wind components above 600hPa and surface moisture (q) error reduction by 7%, indicating enhanced initial conditions. Significant RMSE reductions were observed for wind forecasts at 200 hPa (7.71% on Day 1) and 850 hPa (4.78% on Day 1). Temperature forecasts at 200 hPa exhibited consistent improvements across all forecast days, ranging from 3.05% to 4.70%. Geopotential height forecasts showed the most substantial improvements, with RMSE reductions of 15.33% at 200 hPa (Day 1), and sustained improvements at 850 hPa (3.28%–5.47%) over multiple days. The results highlight the positive effects of integrating high-resolution IPW data into weather models, especially in terms of improving the accuracy of upper-level atmospheric parameters and overall model performance.